Gestures for Positioning, Scaling, and Rotating Virtual Globes
Faisal Agung Abdillah
2025-05-08
Gestures for Globes Research
Data and Libraries Load
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## col_factorlibrary(ARTool)
library(knitr)
data <- read_csv("study_tasks.csv")## Rows: 49244 Columns: 35
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): TaskID, ActionID, distance, direction, complexity, zoomDirection,...
## dbl (22): UserID, main_translation_x, main_translation_y, main_translation_...
## lgl (3): rotateGlobeWhileDragging, oneHandedRotationGesture, moveGlobeWhil...
## dttm (1): Date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.demographic <- read_csv("final_introductory.csv")## Rows: 12 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): Timestamp, Academic_level, Gender, Age_group, Exp_ARVR, Globe_usage...
## dbl (1): UserID
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.positioning_NRG <- read_csv("final_positioning_NRG.csv")## Rows: 12 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Timestamp, Mentally_demanding, Physically_demanding
## dbl (1): UserID
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.positioning_RG <- read_csv("final_positioning_RG.csv")## Rows: 12 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Timestamp, Mentally_demanding, Physically_demanding
## dbl (1): UserID
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.positioning_preference <- read_csv("final_positioning_comparison.csv")## Rows: 12 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Timestamp, Positioning_preference, Positioning_feedback
## dbl (1): UserID
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.rotation_OH <- read_csv("final_rotation_OH.csv")## Rows: 12 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Timestamp, Mentally_demanding, Physically_demanding
## dbl (1): UserID
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.rotation_TH <- read_csv("final_rotation_TH.csv")## Rows: 12 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Timestamp, Mentally_demanding, Physically_demanding
## dbl (1): UserID
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.rotation_preference <- read_csv("final_rotation_comparison.csv")## Rows: 12 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Timestamp, Rotation_preference, Rotation_feedback
## dbl (1): UserID
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.scale_MG <- read_csv("final_scale_MG.csv")## Rows: 12 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Timestamp, Mentally_demanding, Physically_demanding
## dbl (1): UserID
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.scale_NMG <- read_csv("final_scale_NMG.csv")## Rows: 12 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Timestamp, Mentally_demanding, Physically_demanding
## dbl (1): UserID
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.scale_preference <- read_csv("final_scale_comparison.csv")## Rows: 12 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Timestamp, Scale_preference, Scale_feedback
## dbl (1): UserID
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.combined_preference <- read_csv("final_outro_comparison.csv")## Rows: 12 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): Timestamp, Combined_positioning_preference, Combined_rotation_prefe...
## dbl (1): UserID
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.summary(data)## UserID TaskID ActionID
## Min. : 1.000 Length:49244 Length:49244
## 1st Qu.: 4.000 Class :character Class :character
## Median : 7.000 Mode :character Mode :character
## Mean : 6.741
## 3rd Qu.:10.000
## Max. :12.000
## rotateGlobeWhileDragging oneHandedRotationGesture moveGlobeWhileScaling
## Mode :logical Mode :logical Mode :logical
## FALSE:36803 FALSE:11933 FALSE:46552
## TRUE :12441 TRUE :37311 TRUE :2692
##
##
##
## distance direction complexity zoomDirection
## Length:49244 Length:49244 Length:49244 Length:49244
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Date Type ActionStatus
## Min. :2025-04-23 05:27:13.00 Length:49244 Length:49244
## 1st Qu.:2025-04-25 01:36:58.00 Class :character Class :character
## Median :2025-04-26 00:45:01.00 Mode :character Mode :character
## Mean :2025-04-27 21:46:53.98
## 3rd Qu.:2025-05-01 07:26:51.00
## Max. :2025-05-05 23:37:33.00
## main_translation_x main_translation_y main_translation_z main_rotation_x
## Min. :-7.099065 Min. :-0.3298 Min. :-3.487 Min. :-0.97540
## 1st Qu.:-0.400000 1st Qu.: 0.9000 1st Qu.:-1.921 1st Qu.:-0.03161
## Median :-0.004060 Median : 0.9000 Median :-1.500 Median : 0.00000
## Mean :-0.005048 Mean : 1.2326 Mean :-1.683 Mean :-0.03896
## 3rd Qu.: 0.400000 3rd Qu.: 1.5539 3rd Qu.:-1.500 3rd Qu.: 0.00000
## Max. : 3.256168 Max. : 3.8304 Max. : 5.006 Max. : 0.97834
## main_rotation_y main_rotation_z main_rotation_w main_scale_x
## Min. :-1.0000 Min. :-0.97710 Min. :-0.9997261 Min. :0.08431
## 1st Qu.:-0.2033 1st Qu.: 0.00000 1st Qu.: 0.0000001 1st Qu.:0.99989
## Median : 0.9601 Median : 0.00000 Median : 0.0626987 Median :1.00000
## Mean : 0.5003 Mean : 0.01287 Mean : 0.2756917 Mean :0.99575
## 3rd Qu.: 1.0000 3rd Qu.: 0.00000 3rd Qu.: 0.6346812 3rd Qu.:1.00002
## Max. : 1.0000 Max. : 0.98922 Max. : 0.9999814 Max. :7.69231
## main_scale_y main_scale_z target_translation_x target_translation_y
## Min. :0.08431 Min. :0.08431 Min. :-3.10000 Min. :0.613
## 1st Qu.:0.99994 1st Qu.:0.99990 1st Qu.:-0.40000 1st Qu.:0.900
## Median :1.00000 Median :1.00000 Median : 0.00000 Median :0.900
## Mean :0.99577 Mean :0.99576 Mean :-0.02449 Mean :1.245
## 3rd Qu.:1.00002 3rd Qu.:1.00002 3rd Qu.: 0.40000 3rd Qu.:1.773
## Max. :7.69231 Max. :7.69231 Max. : 2.33777 Max. :2.547
## target_translation_z target_rotation_x target_rotation_y target_rotation_z
## Min. :-3.3210 Min. :-0.3928 Min. :-0.6935 Min. :-0.21194
## 1st Qu.:-1.9598 1st Qu.:-0.3584 1st Qu.:-0.5655 1st Qu.: 0.00000
## Median :-1.5000 Median : 0.0000 Median : 1.0000 Median : 0.00000
## Mean :-1.6971 Mean :-0.1153 Mean : 0.3768 Mean :-0.01644
## 3rd Qu.:-1.5000 3rd Qu.: 0.0000 3rd Qu.: 1.0000 3rd Qu.: 0.00000
## Max. :-0.8953 Max. : 0.0000 Max. : 1.0000 Max. : 0.13795
## target_rotation_w target_scale_x target_scale_y target_scale_z
## Min. :-0.9761015 Min. :0.1700 Min. :0.1700 Min. :0.1700
## 1st Qu.: 0.0000001 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:1.0000
## Median : 0.0000001 Median :1.0000 Median :1.0000 Median :1.0000
## Mean : 0.2914215 Mean :0.9946 Mean :0.9946 Mean :0.9946
## 3rd Qu.: 0.7119398 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. : 0.9807853 Max. :2.0000 Max. :2.0000 Max. :2.0000
## match_accuracy_result status
## Min. : 0.00000 Length:49244
## 1st Qu.: 0.00000 Class :character
## Median : 0.00000 Mode :character
## Mean : 0.03784
## 3rd Qu.: 0.00000
## Max. :22.31002summary(demographic)## UserID Timestamp Academic_level Gender
## Min. : 1.00 Length:12 Length:12 Length:12
## 1st Qu.: 3.75 Class :character Class :character Class :character
## Median : 6.50 Mode :character Mode :character Mode :character
## Mean : 6.50
## 3rd Qu.: 9.25
## Max. :12.00
## Age_group Exp_ARVR Globe_usage_frequency
## Length:12 Length:12 Length:12
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## Have_used_VisionPro
## Length:12
## Class :character
## Mode :character
##
##
## summary(positioning_NRG)## UserID Timestamp Mentally_demanding Physically_demanding
## Min. : 1.00 Length:12 Length:12 Length:12
## 1st Qu.: 3.75 Class :character Class :character Class :character
## Median : 6.50 Mode :character Mode :character Mode :character
## Mean : 6.50
## 3rd Qu.: 9.25
## Max. :12.00summary(positioning_RG)## UserID Timestamp Mentally_demanding Physically_demanding
## Min. : 1.00 Length:12 Length:12 Length:12
## 1st Qu.: 3.75 Class :character Class :character Class :character
## Median : 6.50 Mode :character Mode :character Mode :character
## Mean : 6.50
## 3rd Qu.: 9.25
## Max. :12.00summary(positioning_preference)## UserID Timestamp Positioning_preference Positioning_feedback
## Min. : 1.00 Length:12 Length:12 Length:12
## 1st Qu.: 3.75 Class :character Class :character Class :character
## Median : 6.50 Mode :character Mode :character Mode :character
## Mean : 6.50
## 3rd Qu.: 9.25
## Max. :12.00summary(rotation_OH)## UserID Timestamp Mentally_demanding Physically_demanding
## Min. : 1.00 Length:12 Length:12 Length:12
## 1st Qu.: 3.75 Class :character Class :character Class :character
## Median : 6.50 Mode :character Mode :character Mode :character
## Mean : 6.50
## 3rd Qu.: 9.25
## Max. :12.00summary(rotation_TH)## UserID Timestamp Mentally_demanding Physically_demanding
## Min. : 1.00 Length:12 Length:12 Length:12
## 1st Qu.: 3.75 Class :character Class :character Class :character
## Median : 6.50 Mode :character Mode :character Mode :character
## Mean : 6.50
## 3rd Qu.: 9.25
## Max. :12.00summary(rotation_preference)## UserID Timestamp Rotation_preference Rotation_feedback
## Min. : 1.00 Length:12 Length:12 Length:12
## 1st Qu.: 3.75 Class :character Class :character Class :character
## Median : 6.50 Mode :character Mode :character Mode :character
## Mean : 6.50
## 3rd Qu.: 9.25
## Max. :12.00summary(scale_MG)## UserID Timestamp Mentally_demanding Physically_demanding
## Min. : 1.00 Length:12 Length:12 Length:12
## 1st Qu.: 3.75 Class :character Class :character Class :character
## Median : 6.50 Mode :character Mode :character Mode :character
## Mean : 6.50
## 3rd Qu.: 9.25
## Max. :12.00summary(scale_NMG)## UserID Timestamp Mentally_demanding Physically_demanding
## Min. : 1.00 Length:12 Length:12 Length:12
## 1st Qu.: 3.75 Class :character Class :character Class :character
## Median : 6.50 Mode :character Mode :character Mode :character
## Mean : 6.50
## 3rd Qu.: 9.25
## Max. :12.00summary(scale_preference)## UserID Timestamp Scale_preference Scale_feedback
## Min. : 1.00 Length:12 Length:12 Length:12
## 1st Qu.: 3.75 Class :character Class :character Class :character
## Median : 6.50 Mode :character Mode :character Mode :character
## Mean : 6.50
## 3rd Qu.: 9.25
## Max. :12.00summary(combined_preference)## UserID Timestamp Combined_positioning_preference
## Min. : 1.00 Length:12 Length:12
## 1st Qu.: 3.75 Class :character Class :character
## Median : 6.50 Mode :character Mode :character
## Mean : 6.50
## 3rd Qu.: 9.25
## Max. :12.00
## Combined_rotation_preference Combined_scale_preference Combined_feedback
## Length:12 Length:12 Length:12
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
## Participants Demographic Information
# Total number of participants
length(unique(data$UserID))## [1] 12# Participants' gender distribution
demographic.gender <- demographic %>%
select(UserID, Gender) %>%
distinct() %>%
group_by(Gender) %>%
summarise(count = n()) %>%
mutate(percentage = round(count / sum(count) * 100, 1), percentage = paste0(percentage, "%"))
demographic.gender## # A tibble: 2 × 3
## Gender count percentage
## <chr> <int> <chr>
## 1 Man 10 83.3%
## 2 Woman 2 16.7%# Participants' gender distribution chart
ggplot(demographic.gender, aes(x = "", y = count, fill = Gender)) +
geom_col(width = 1, color = "white") +
coord_polar(theta = "y") +
geom_text(aes(label = percentage), position = position_stack(vjust = 0.5), size = 4) +
labs(title = "Distribution of Participants' Gender") +
theme_void()
# Participants' academic level distribution
demographic.academic_level <- demographic %>%
select(UserID, Academic_level) %>%
distinct() %>%
group_by(Academic_level) %>%
summarise(count = n()) %>%
mutate(percentage = round(count / sum(count) * 100, 1), graph_label = paste0(percentage, "%")) %>%
rename(`Academic levels` = Academic_level)
demographic.academic_level## # A tibble: 3 × 4
## `Academic levels` count percentage graph_label
## <chr> <int> <dbl> <chr>
## 1 Graduate Student 10 83.3 83.3%
## 2 Postdoctoral Researcher 1 8.3 8.3%
## 3 Undergraduate Student 1 8.3 8.3%# Participants' academic level distribution chart
ggplot(demographic.academic_level, aes(x = "", y = count, fill = `Academic levels`)) +
geom_col(width = 1, color = "white") +
coord_polar(theta = "y") +
geom_text(aes(label = graph_label), position = position_stack(vjust = 0.5), size = 4) +
labs(title = "Distribution of Participants' Academic Level") +
theme_void() 
# Participants' previous AR/VR experience distribution
demographic.ARVR_exp <- demographic %>%
select(UserID, Exp_ARVR ) %>%
distinct() %>%
group_by(Exp_ARVR) %>%
summarise(count = n()) %>%
mutate(percentage = round(count / sum(count) * 100, 1),
label = paste0(percentage, "%"),
ShortLabel = fct_recode(Exp_ARVR,
"No experience" = "I have no experience")
) %>%
rename(`Previous AR/VR experience` = ShortLabel)
demographic.ARVR_exp## # A tibble: 3 × 5
## Exp_ARVR count percentage label Previous AR/VR exper…¹
## <chr> <int> <dbl> <chr> <fct>
## 1 Beginner (less than 5 hours exp… 4 33.3 33.3% Beginner (less than 5…
## 2 Familiar (5-20 hours experience) 3 25 25% Familiar (5-20 hours …
## 3 I have no experience 5 41.7 41.7% No experience
## # ℹ abbreviated name: ¹`Previous AR/VR experience`# Participants' previous AR/VR experience distribution chart
ggplot(demographic.ARVR_exp, aes(x = "", y = count, fill = `Previous AR/VR experience`)) +
geom_col(width = 1, color = "white") +
coord_polar(theta = "y") +
geom_text(aes(label = label), position = position_stack(vjust = 0.5), size = 4) +
labs(title = "Distribution of Participants Previous AR/VR Experience") +
theme_void() 
# Participants' previous globe experience distribution
demographic.globes_exp <- demographic %>%
select(UserID, Globe_usage_frequency) %>%
distinct() %>%
group_by(Globe_usage_frequency) %>%
summarise(count = n()) %>%
mutate(percentage = round(count / sum(count) * 100, 1),
graph_label = paste0(percentage, "%")) %>%
rename(`Previous globes experience` = Globe_usage_frequency)
demographic.globes_exp## # A tibble: 3 × 4
## `Previous globes experience` count percentage graph_label
## <chr> <int> <dbl> <chr>
## 1 A few times a month 1 8.3 8.3%
## 2 A few times a year 3 25 25%
## 3 Once every few years 8 66.7 66.7%# Participants' previous globe experience distribution chart
ggplot(demographic.globes_exp, aes(x = "", y = count, fill = `Previous globes experience`)) +
geom_col(width = 1, color = "white") +
coord_polar(theta = "y") +
geom_text(aes(label = graph_label), position = position_stack(vjust = 0.5), size = 4) +
labs(title = "Distribution of Participants Previous AR/VR Experience") +
theme_void() 
# Participants' previous Apple Vision Pro Experience distribution
demographic.visionpro_exp <- demographic %>%
select(UserID, Have_used_VisionPro) %>%
distinct() %>%
group_by(Have_used_VisionPro) %>%
summarise(count = n()) %>%
mutate(
percentage = round(count / sum(count) * 100, 1),
graph_label = paste0(percentage, "%")
) %>%
rename(`Have used Apple Vision Pro` = Have_used_VisionPro)
demographic.visionpro_exp## # A tibble: 2 × 4
## `Have used Apple Vision Pro` count percentage graph_label
## <chr> <int> <dbl> <chr>
## 1 I have never used the Apple Vision Pro 11 91.7 91.7%
## 2 I have used the Apple Vision Pro once or twice 1 8.3 8.3%# Participants' previous Apple Vision Pro Experience distribution chart
ggplot(demographic.visionpro_exp, aes(x = "", y = count, fill = `Have used Apple Vision Pro`)) +
geom_col(width = 1, color = "white") +
coord_polar(theta = "y") +
geom_text(aes(label = graph_label), position = position_stack(vjust = 0.5), size = 4) +
labs(title = "Distribution of Participants Previous AR/VR Experience") +
theme_void() 
Notes: Use boxplot instead of bar chart Add technique column
Structure it like this:
Study: Positioning
Positioning Data Preparation
data.positioning <- data %>%
inner_join(demographic, by = "UserID") %>%
inner_join(positioning_NRG, by = "UserID") %>%
rename(
PAAS_NRG = Mentally_demanding,
BORG_NRG = Physically_demanding
) %>%
mutate(
PAAS_NRG = as.numeric(str_extract(PAAS_NRG, "\\d+(\\.\\d+)?")),
BORG_NRG = as.numeric(str_extract(BORG_NRG, "\\d+(\\.\\d+)?"))
) %>%
inner_join(positioning_RG, by = "UserID") %>%
rename(
PAAS_RG = Mentally_demanding,
BORG_RG = Physically_demanding
) %>%
mutate(
PAAS_RG = as.numeric(str_extract(PAAS_RG, "\\d+(\\.\\d+)?")),
BORG_RG = as.numeric(str_extract(BORG_RG, "\\d+(\\.\\d+)?"))
) %>%
inner_join(positioning_preference, by = "UserID") %>%
rename(
behaviour_preference = Positioning_preference,
behaviour_feedback = Positioning_feedback
) %>%
mutate(
behaviour_preference = case_when(
str_detect(behaviour_preference, "Static orientation") ~ "staticOrientation",
str_detect(behaviour_preference, "Adaptive orientation") ~ "adaptiveOrientation",
str_detect(behaviour_preference, "no preference") ~ "noPreference",
TRUE ~ "unknown"
) ) %>%
filter(Type == "positionTask") %>%
select(UserID, TaskID, ActionID, rotateGlobeWhileDragging, distance, direction, Date, ActionStatus, main_translation_x,
main_translation_y, main_translation_z, target_translation_x, target_translation_y, target_translation_z,
match_accuracy_result, status, PAAS_NRG, BORG_NRG, PAAS_RG, BORG_RG, behaviour_preference, behaviour_feedback) %>%
mutate(positionCondition = if_else(rotateGlobeWhileDragging, "rotatingGlobe", "nonRotatingGlobe")) %>%
select(-rotateGlobeWhileDragging) %>%
mutate(distance = as.factor(distance),
direction = as.factor(direction),
positionCondition = as.factor(positionCondition),
status = as.factor(status),
behaviour_preference = as.factor(behaviour_preference))Position Task Study
### Accuracy
#### Normality
data.positioning.matched <- data.positioning %>%
filter(status == "Matched")
shapiro.test(data.positioning.matched$match_accuracy_result)##
## Shapiro-Wilk normality test
##
## data: data.positioning.matched$match_accuracy_result
## W = 0.97029, p-value = 2.086e-09hist(data.positioning.matched$match_accuracy_result, breaks = 100,
main = "Histogram (Zoomed)", xlab = "Accuracy",
col = "lightblue", xlim = c(0, 0.06))
plot(density(data.positioning.matched$match_accuracy_result),
main = "Density Plot (Zoomed)", xlab = "Accuracy",
col = "blue", lwd = 2, xlim = c(0, 0.6))
# Although the w value is close to 1, the p value is below 0.05 so we reject null hypothesis that the data is normally distributed
# So, we cannot use one way ANOVA, instead, we use Wilcoxon signed-rank test
#### Statistical tests
data.positioning.matched.accuracy_avg.long <- data.positioning.matched %>%
group_by(UserID, positionCondition) %>%
summarise(mean_accuracy = mean(match_accuracy_result, na.rm = TRUE), .groups = 'drop')
data.positioning.matched.art <- art(mean_accuracy ~ positionCondition + (1|UserID), data = data.positioning.matched.accuracy_avg.long)
anova(data.positioning.matched.art)## Analysis of Variance of Aligned Rank Transformed Data
##
## Table Type: Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## Model: Mixed Effects (lmer)
## Response: art(mean_accuracy)
##
## F Df Df.res Pr(>F)
## 1 positionCondition 0.33588 1 11 0.5739
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Factor tested: positionCondition (e.g., rotatingGlobe vs nonRotatingGlobe)
# F-statistic: 0.33588 — this tells us the ratio of variance between the groups to the variance within groups (after aligned rank transformation).
# Degrees of freedom (df): 1 for the factor, and 11 for the residuals (which likely means 12 participants).
# p-value: 0.5739 — this is not statistically significant at any common threshold (e.g., 0.05).
# An ART ANOVA revealed no significant effect of position condition (rotating vs non-rotating) on match accuracy, F(1, 11) = 0.34, p = .574.
ggplot(data.positioning.matched.accuracy_avg.long, aes(x = positionCondition, y = mean_accuracy, group = UserID)) +
geom_line(aes(color = as.factor(UserID))) +
geom_point(size = 3) +
labs(title = "Paired Accuracy: Moving vs Non-Moving Globe",
x = "Condition",
y = "Match Accuracy") +
theme_minimal()
ggplot(data.positioning.matched.accuracy_avg.long, aes(x = positionCondition, y = mean_accuracy)) +
geom_boxplot(outlier.shape = NA, fill = "lightblue") +
geom_jitter(width = 0.1, size = 2, alpha = 0.7) +
labs(title = "Accuracy by Globe Movement Condition",
x = "Condition",
y = "Match Accuracy") +
theme_minimal()
### Completion Time
data.positioning.taskCompletion_avg <- data.positioning %>%
group_by(UserID, TaskID) %>%
summarise(
completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
.groups = "drop"
)
shapiro.test(data.positioning.taskCompletion_avg$completion_time)##
## Shapiro-Wilk normality test
##
## data: data.positioning.taskCompletion_avg$completion_time
## W = 0.59479, p-value < 2.2e-16data.positioning.taskCompletion_avg.long <- data.positioning %>%
group_by(UserID, positionCondition, TaskID) %>%
summarise(
completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
.groups = "drop"
) %>%
group_by(UserID, positionCondition) %>%
summarise(
avg_completion_time = mean(completion_time),
.groups = "drop"
)
# %>%
# pivot_wider(names_from = positionCondition, values_from = avg_completion_time)
# wilcox.test(
# data.positioning.taskCompletion_avg.wide$rotatingGlobe,
# data.positioning.taskCompletion_avg.wide$nonRotatingGlobe,
# paired = TRUE,
# alternative = "two.sided"
# )
data.positioning.taskCompletion_avg.art <- art(avg_completion_time ~ positionCondition + (1|UserID), data = data.positioning.taskCompletion_avg.long)
anova(data.positioning.taskCompletion_avg.art)## Analysis of Variance of Aligned Rank Transformed Data
##
## Table Type: Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## Model: Mixed Effects (lmer)
## Response: art(avg_completion_time)
##
## F Df Df.res Pr(>F)
## 1 positionCondition 0.26366 1 11 0.61777
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# The test statistic is F(1, 11) = 0.264, with a p-value of 0.618.
# Since p > 0.05, the result is not statistically significant.
# This means that there is no evidence of a significant effect of positionCondition on avg_completion_time.
#
# An aligned rank transform ANOVA showed that position condition did not significantly affect average task completion time, F(1, 11) = 0.26, p = .618.
# data.positioning.taskCompletion_avg.long <- data.positioning.taskCompletion_avg.wide %>%
# pivot_longer(cols = c(rotatingGlobe, nonRotatingGlobe),
# names_to = "Condition",
# values_to = "completion_time")
ggplot(data.positioning.taskCompletion_avg.long, aes(x = positionCondition, y = avg_completion_time, group = UserID)) +
geom_line(aes(color = as.factor(UserID)), linewidth = 1, alpha = 0.6) +
geom_point(size = 3) +
labs(
title = "Task Completion Time by Condition",
x = "Condition",
y = "Completion Time (minutes)",
color = "UserID"
) +
theme_minimal()
ggplot(data.positioning.taskCompletion_avg.long, aes(x = positionCondition, y = avg_completion_time)) +
geom_boxplot(outlier.shape = NA, fill = "lightblue") +
geom_jitter(width = 0.1, size = 2, alpha = 0.7) +
labs(title = "Accuracy by Globe Movement Condition",
x = "Condition",
y = "Match Accuracy") +
theme_minimal()
### Subjective Measures
#### Physical and Mental Exertion
# Spearman’s rank correlation is a non-parametric test.
# It does not assume normal distribution of the variables.
# It works on ranks of the data, not the raw values — so it’s robust against skewed or non-normal distributions.
data.positioning.matched.RG <- data.positioning.matched %>%
filter(positionCondition == "rotatingGlobe")
cor.test(data.positioning.matched.RG$BORG_RG,
data.positioning.matched.RG$match_accuracy_result, method = "spearman")## Warning in cor.test.default(data.positioning.matched.RG$BORG_RG,
## data.positioning.matched.RG$match_accuracy_result, : Cannot compute exact
## p-value with ties##
## Spearman's rank correlation rho
##
## data: data.positioning.matched.RG$BORG_RG and data.positioning.matched.RG$match_accuracy_result
## S = 3447428, p-value = 0.02285
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1340871ggplot(data.positioning.matched.RG, aes(x = BORG_RG, y = match_accuracy_result)) +
geom_point(color = "steelblue", size = 2, alpha = 0.7) +
geom_smooth(method = "loess", color = "darkred", se = TRUE) +
labs(
title = "Correlation between Physical Exertion and Accuracy (Rotating Globe)",
x = "Physical Exertion (BORG_RG)",
y = "Match Accuracy Result"
) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
data.positioning.matched.NRG <- data.positioning.matched %>%
filter(positionCondition == "nonRotatingGlobe")
cor.test(data.positioning.matched.NRG$BORG_NRG,
data.positioning.matched.NRG$match_accuracy_result, method = "spearman")## Warning in cor.test.default(data.positioning.matched.NRG$BORG_NRG,
## data.positioning.matched.NRG$match_accuracy_result, : Cannot compute exact
## p-value with ties##
## Spearman's rank correlation rho
##
## data: data.positioning.matched.NRG$BORG_NRG and data.positioning.matched.NRG$match_accuracy_result
## S = 3491838, p-value = 0.03706
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1229323ggplot(data.positioning.matched.NRG, aes(x = BORG_NRG, y = match_accuracy_result)) +
geom_point(color = "steelblue", size = 2, alpha = 0.7) +
geom_smooth(method = "loess", color = "darkred", se = TRUE) +
labs(
title = "Correlation between Physical Exertion and Accuracy (Non-rotating Globe)",
x = "Physical Exertion (BORG_NRG)",
y = "Match Accuracy Result"
) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at -0.02## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 3.02## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 1.74e-16## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## -0.02## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 3.02## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 1.74e-16## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4
data.positioning.taskCompletion_avg.RG <- data.positioning %>%
group_by(UserID, positionCondition, PAAS_RG, BORG_RG, TaskID) %>%
summarise(
completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
.groups = "drop"
) %>%
filter(positionCondition == "rotatingGlobe")
cor.test(data.positioning.taskCompletion_avg.RG$BORG_RG,
data.positioning.taskCompletion_avg.RG$completion_time, method = "spearman")## Warning in cor.test.default(data.positioning.taskCompletion_avg.RG$BORG_RG, :
## Cannot compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: data.positioning.taskCompletion_avg.RG$BORG_RG and data.positioning.taskCompletion_avg.RG$completion_time
## S = 3103888, p-value = 0.0001632
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2203763data.positioning.taskCompletion_avg.NRG <- data.positioning %>%
group_by(UserID, positionCondition, PAAS_NRG, BORG_NRG, TaskID) %>%
summarise(
completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
.groups = "drop"
) %>%
filter(positionCondition == "nonRotatingGlobe")
cor.test(data.positioning.taskCompletion_avg.NRG$BORG_NRG,
data.positioning.taskCompletion_avg.NRG$completion_time, method = "spearman")## Warning in cor.test.default(data.positioning.taskCompletion_avg.NRG$BORG_NRG, :
## Cannot compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: data.positioning.taskCompletion_avg.NRG$BORG_NRG and data.positioning.taskCompletion_avg.NRG$completion_time
## S = 4081328, p-value = 0.671
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.02513373# PAAS vs Accuracy
data.positioning.matched.RG$condition <- "RG"
data.positioning.matched.NRG$condition <- "NRG"
data.positioning.matched.PAAS_combined <- bind_rows(
data.positioning.matched.RG %>% rename(PAAS = PAAS_RG),
data.positioning.matched.NRG %>% rename(PAAS = PAAS_NRG)
)
ggplot(data.positioning.matched.PAAS_combined, aes(x = PAAS, y = match_accuracy_result, color = condition)) +
geom_point(alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE) +
labs(
title = "Mental Exertion vs Match Accuracy in Positioning",
x = "PAAS Scale",
y = "Match Accuracy",
color = "Condition"
) +
scale_color_manual(values = c("RG" = "blue", "NRG" = "red")) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
# PAAS vs Completion Time
data.positioning.taskCompletion_avg.RG$condition <- "RG"
data.positioning.taskCompletion_avg.NRG$condition <- "NRG"
data.positioning.taskCompletion_avg.PAAS_combined <- bind_rows(
data.positioning.taskCompletion_avg.RG %>%
rename(PAAS = PAAS_RG),
data.positioning.taskCompletion_avg.NRG %>%
rename(PAAS = PAAS_NRG)
)
ggplot(data.positioning.taskCompletion_avg.PAAS_combined, aes(x = PAAS, y = completion_time, color = condition)) +
geom_point(alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE) +
labs(
title = "Mental Exertion vs Completion Time in Positioning",
x = "PASS Scale",
y = "Completion Time",
color = "Condition"
) +
scale_color_manual(values = c("RG" = "blue", "NRG" = "red")) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
# BORG vs Accuracy
data.positioning.matched.RG$condition <- "RG"
data.positioning.matched.NRG$condition <- "NRG"
data.positioning.matched.BORG_combined <- bind_rows(
data.positioning.matched.RG %>% rename(BORG = BORG_RG),
data.positioning.matched.NRG %>% rename(BORG = BORG_NRG)
)
ggplot(data.positioning.matched.BORG_combined, aes(x = BORG, y = match_accuracy_result, color = condition)) +
geom_point(alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE) +
labs(
title = "Physical Exertion vs Match Accuracy in Positioning",
x = "BORG Scale",
y = "Match Accuracy",
color = "Condition"
) +
scale_color_manual(values = c("RG" = "blue", "NRG" = "red")) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
# BORG vs Completion Time
data.positioning.taskCompletion_avg.RG$condition <- "RG"
data.positioning.taskCompletion_avg.NRG$condition <- "NRG"
data.positioning.taskCompletion_avg.BORG_combined <- bind_rows(
data.positioning.taskCompletion_avg.RG %>%
rename(BORG = BORG_RG),
data.positioning.taskCompletion_avg.NRG %>%
rename(BORG = BORG_NRG)
)
ggplot(data.positioning.taskCompletion_avg.BORG_combined, aes(x = BORG, y = completion_time, color = condition)) +
geom_point(alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE) +
labs(
title = "Physical Exertion vs Completion Time in Positioning",
x = "BORG Scale",
y = "Completion Time",
color = "Condition"
) +
scale_color_manual(values = c("RG" = "blue", "NRG" = "red")) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
#### Preference
data.positioning %>%
select(UserID, behaviour_preference) %>%
distinct() %>%
count(behaviour_preference) %>%
mutate(
percent = n / sum(n),
ncount = paste0(n, "\n", percent_format()(percent))
) %>%
ggplot(aes(x = "", y = n, fill = behaviour_preference)) +
geom_col(width = 1, color = "white") +
coord_polar(theta = "y") +
geom_text(aes(label = ncount), position = position_stack(vjust = 0.5), size = 4) +
labs(
title = "Distribution of Positioning Behaviour Preferences",
fill = "Preference"
) +
theme_void()
#### Comments
data.positioning.preference.summary <- data.positioning %>%
mutate(
behaviour_preference = case_when(
behaviour_preference == "staticOrientation" ~ "Static Orientation",
behaviour_preference == "adaptiveOrientation" ~ "Adaptive Orientation",
behaviour_preference == "noPreference" ~ "No Preference",
TRUE ~ behaviour_preference
)
) %>%
group_by(UserID) %>%
summarise(
behaviour_preference = first(behaviour_preference),
behaviour_feedback = first(behaviour_feedback),
.groups = "drop"
)
kable(data.positioning.preference.summary, caption = "User Feedback Summary - Positioning")| UserID | behaviour_preference | behaviour_feedback |
|---|---|---|
| 1 | Static Orientation | I prefer the static orientation as it makes me feel more enjoyable and easy to move it. However, in relation to moving the globe, I think, gaze is effective enough but the pinch gesture must be changed into other gestures such as thumb movement. |
| 2 | Adaptive Orientation | Static orientation give me a little bit of nausea. And regarding the control, the x and y axis gesture is easy to control, but for both negative and positive z-axis is a bit hard to do since its depends on my hand’s position and hand’s length. |
| 3 | Static Orientation | Static is more intuitove because it only display 1 type of direction to control than adaptive. In order to rotate the globe, i suggest to introduce one more gesture where we can pinch and rotate finger at the same time. |
| 4 | Static Orientation | I like it when it static it is more dynamic and realistic like a globe should be, to move the globe I think it would be better if we can pinch and throw the globe to the designated positions. or maybe we can move the globe by the palm of our hands. |
| 5 | Adaptive Orientation | It’s easier to see the same side and of the earth. It will be better if I can grab it like a real globe |
| 6 | Static Orientation | I prefer the static one because it more realistic, more natural.its more convenient to observe, and it feels like we use real globe. I feel the gaze and pinch method is better that’s directly touch, it is also less prone to errors. |
| 7 | Adaptive Orientation | I prefer the adaptive orientation one because it remains focused and detailed. |
| 8 | Static Orientation | The static one feels more real like physical globe. It would be convinient if there is a frame like a physical globe where we can move the globe around with that. |
| 9 | Static Orientation | I prefer the static one because its more intuitive |
| 10 | Adaptive Orientation | I prefer adaptive one because its easier to observe the surface. |
| 11 | Static Orientation | I prefer static because its easier to focus on the globe and less confusing |
| 12 | No Preference | 1. It depends on the situation, if the situation doesn’t require me to actually show the globe to other people, I wouldn’t mind if it doesn’t move. But, if it requires me to show other people (live presentation), I would want it to adaptively look towards me every time. Because if not I need to always adjust the orientation. |
- The DPI needs to be adaptive, my preference is to pinch the globe and move it towards the destination. But, it always falls shorter than it should have been.
- I had difficulty in selecting the main globe when it is obstructed by the target globe. This is due to the need to repinch when my hand is out of space. |
### SummaryStudy: Rotating
Rotating Data Preparation
data.rotating <- data %>%
inner_join(demographic, by = "UserID") %>%
inner_join(rotation_OH, by = "UserID") %>%
rename(
PAAS_OH = Mentally_demanding,
BORG_OH = Physically_demanding
) %>%
mutate(
PAAS_OH = as.numeric(str_extract(PAAS_OH, "\\d+(\\.\\d+)?")),
BORG_OH = as.numeric(str_extract(BORG_OH, "\\d+(\\.\\d+)?"))
) %>%
inner_join(rotation_TH, by = "UserID") %>%
rename(
PAAS_TH = Mentally_demanding,
BORG_TH = Physically_demanding
) %>%
mutate(
PAAS_TH = as.numeric(str_extract(PAAS_TH, "\\d+(\\.\\d+)?")),
BORG_TH = as.numeric(str_extract(BORG_TH, "\\d+(\\.\\d+)?"))
) %>%
inner_join(rotation_preference, by = "UserID") %>%
rename(
behaviour_preference = Rotation_preference,
behaviour_feedback = Rotation_feedback
) %>%
mutate(
behaviour_preference = case_when(
str_detect(behaviour_preference, "One-handed") ~ "oneHandedPreference",
str_detect(behaviour_preference, "Two-handed") ~ "twoHandedPreference",
str_detect(behaviour_preference, "no preference") ~ "noPreference",
TRUE ~ "unknown"
)) %>%
filter(Type == "rotationTask") %>%
select(UserID, TaskID, ActionID, oneHandedRotationGesture, complexity, Date, ActionStatus, main_rotation_x,
main_rotation_y, main_rotation_z, main_rotation_w, target_rotation_x, target_rotation_y, target_rotation_z,
target_rotation_w,match_accuracy_result, status, PAAS_OH, BORG_OH, PAAS_TH, BORG_TH, behaviour_preference, behaviour_feedback) %>%
mutate(rotationCondition = if_else(oneHandedRotationGesture, "oneHanded", "twoHanded")) %>%
select(-oneHandedRotationGesture) %>%
mutate(complexity = as.factor(complexity),
rotationCondition = as.factor(rotationCondition),
status = as.factor(status),
behaviour_preference = as.factor(behaviour_preference))Rotation Task Study
### Accuracy
#### Normality
data.rotating.matched <- data.rotating %>%
filter(status == "Matched")
shapiro.test(data.rotating.matched$match_accuracy_result)##
## Shapiro-Wilk normality test
##
## data: data.rotating.matched$match_accuracy_result
## W = 0.94156, p-value = 5.023e-07hist(data.rotating.matched$match_accuracy_result, breaks = 100,
main = "Histogram (Zoomed)", xlab = "Accuracy",
col = "lightblue", xlim = c(0, 0.5))
plot(density(data.rotating.matched$match_accuracy_result),
main = "Density Plot (Zoomed)", xlab = "Accuracy",
col = "blue", lwd = 2, xlim = c(0, 0.5))
# Although the w value is close to 1, the p value is below 0.05 so we reject null hypothesis that the data is normally distributed
# So, we cannot use one way ANOVA, instead, we use Wilcoxon signed-rank test
#### Statistical tests
data.rotating.matched.accuracy_avg.long <- data.rotating.matched %>%
group_by(UserID, rotationCondition) %>%
summarise(mean_accuracy = mean(match_accuracy_result, na.rm = TRUE), .groups = 'drop')
data.rotating.matched.art <- art(mean_accuracy ~ rotationCondition + (1|UserID), data = data.rotating.matched.accuracy_avg.long)
anova(data.rotating.matched.art)## boundary (singular) fit: see help('isSingular')## Analysis of Variance of Aligned Rank Transformed Data
##
## Table Type: Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## Model: Mixed Effects (lmer)
## Response: art(mean_accuracy)
##
## F Df Df.res Pr(>F)
## 1 rotationCondition 6.2209 1 11 0.029814 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Because p < .05, the result is considered statistically significant.
# Therefore, we reject the null hypothesis and conclude that rotation affects accuracy.
# An ART ANOVA revealed a significant main effect of rotation condition on mean accuracy, F(1, 11) = 6.22, p = .030, indicating that the presence of globe rotation influenced the participants’ accuracy during the task.
ggplot(data.rotating.matched.accuracy_avg.long, aes(x = rotationCondition, y = mean_accuracy, group = UserID)) +
geom_line(aes(color = as.factor(UserID))) +
geom_point(size = 3) +
labs(title = "Paired Accuracy: Moving vs Non-Moving Globe",
x = "Condition",
y = "Match Accuracy") +
theme_minimal()
ggplot(data.rotating.matched.accuracy_avg.long, aes(x = rotationCondition, y = mean_accuracy)) +
geom_boxplot(outlier.shape = NA, fill = "lightblue") +
geom_jitter(width = 0.1, size = 2, alpha = 0.7) +
labs(title = "Accuracy by Globe Movement Condition",
x = "Condition",
y = "Match Accuracy") +
theme_minimal()
### Completion Time
data.rotating.taskCompletion_avg <- data.rotating %>%
group_by(UserID, TaskID) %>%
summarise(
completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
.groups = "drop"
)
shapiro.test(data.rotating.taskCompletion_avg$completion_time)##
## Shapiro-Wilk normality test
##
## data: data.rotating.taskCompletion_avg$completion_time
## W = 0.49195, p-value < 2.2e-16data.rotating.taskCompletion_avg.long <- data.rotating %>%
group_by(UserID, rotationCondition, TaskID) %>%
summarise(
completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
.groups = "drop"
) %>%
group_by(UserID, rotationCondition) %>%
summarise(
avg_completion_time = mean(completion_time),
.groups = "drop"
)
data.rotating.taskCompletion_avg.art <- art(avg_completion_time ~ rotationCondition + (1|UserID), data = data.rotating.taskCompletion_avg.long)
anova(data.rotating.taskCompletion_avg.art)## Analysis of Variance of Aligned Rank Transformed Data
##
## Table Type: Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## Model: Mixed Effects (lmer)
## Response: art(avg_completion_time)
##
## F Df Df.res Pr(>F)
## 1 rotationCondition 6.4517 1 11 0.027479 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# F(1, 11) = 6.45, p = 0.027
# Since the p-value is less than 0.05, the result is statistically significant.
# This means that rotationCondition has a significant effect on avg_completion_time.
#
# An aligned rank transform ANOVA revealed a significant effect of rotation condition on task completion time, F(1, 11) = 6.45, p = .027.
ggplot(data.rotating.taskCompletion_avg.long, aes(x = rotationCondition, y = avg_completion_time, group = UserID)) +
geom_line(aes(color = as.factor(UserID)), linewidth = 1, alpha = 0.6) +
geom_point(size = 3) +
labs(
title = "Task Completion Time by Condition",
x = "Condition",
y = "Completion Time (minutes)",
color = "UserID"
) +
theme_minimal()
ggplot(data.rotating.taskCompletion_avg.long, aes(x = rotationCondition, y = avg_completion_time)) +
geom_boxplot(outlier.shape = NA, fill = "lightblue") +
geom_jitter(width = 0.1, size = 2, alpha = 0.7) +
labs(title = "Accuracy by Globe Movement Condition",
x = "Condition",
y = "Match Accuracy") +
theme_minimal()
### Subjective Measures
#### Physical and Mental Exertion
# Spearman’s rank correlation is a non-parametric test.
# It does not assume normal distribution of the variables.
# It works on ranks of the data, not the raw values — so it’s robust against skewed or non-normal distributions.
data.rotating.matched.OH <- data.rotating.matched %>%
filter(rotationCondition == "oneHanded")
cor.test(data.rotating.matched.OH$BORG_OH,
data.rotating.matched.OH$match_accuracy_result, method = "spearman")## Warning in cor.test.default(data.rotating.matched.OH$BORG_OH,
## data.rotating.matched.OH$match_accuracy_result, : Cannot compute exact p-value
## with ties##
## Spearman's rank correlation rho
##
## data: data.rotating.matched.OH$BORG_OH and data.rotating.matched.OH$match_accuracy_result
## S = 152593, p-value = 0.7353
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.0349492ggplot(data.rotating.matched.OH, aes(x = BORG_OH, y = match_accuracy_result)) +
geom_point(color = "steelblue", size = 2, alpha = 0.7) +
geom_smooth(method = "loess", color = "darkred", se = TRUE) +
labs(
title = "Correlation between Physical Exertion and Accuracy (One Handed)",
x = "Physical Exertion (BORG_OH)",
y = "Match Accuracy Result"
) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 5.4581e-17## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 1## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 5.4581e-17
data.rotating.matched.TH <- data.rotating.matched %>%
filter(rotationCondition == "twoHanded")
cor.test(data.rotating.matched.TH$BORG_TH,
data.rotating.matched.TH$match_accuracy_result, method = "spearman")## Warning in cor.test.default(data.rotating.matched.TH$BORG_TH,
## data.rotating.matched.TH$match_accuracy_result, : Cannot compute exact p-value
## with ties##
## Spearman's rank correlation rho
##
## data: data.rotating.matched.TH$BORG_TH and data.rotating.matched.TH$match_accuracy_result
## S = 133640, p-value = 0.3644
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.09359448ggplot(data.rotating.matched.TH, aes(x = BORG_TH, y = match_accuracy_result)) +
geom_point(color = "steelblue", size = 2, alpha = 0.7) +
geom_smooth(method = "loess", color = "darkred", se = TRUE) +
labs(
title = "Correlation between Physical Exertion and Accuracy (Two Handed)",
x = "Physical Exertion (BORG_TH)",
y = "Match Accuracy Result"
) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
data.rotating.taskCompletion_avg.OH <- data.rotating %>%
group_by(UserID, rotationCondition, PAAS_OH, BORG_OH, TaskID) %>%
summarise(
completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
.groups = "drop"
) %>%
filter(rotationCondition == "oneHanded")
cor.test(data.rotating.taskCompletion_avg.OH$BORG_OH,
data.rotating.taskCompletion_avg.OH$completion_time, method = "spearman")## Warning in cor.test.default(data.rotating.taskCompletion_avg.OH$BORG_OH, :
## Cannot compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: data.rotating.taskCompletion_avg.OH$BORG_OH and data.rotating.taskCompletion_avg.OH$completion_time
## S = 139512, p-value = 0.6029
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.05376892data.rotating.taskCompletion_avg.TH <- data.rotating %>%
group_by(UserID, rotationCondition, PAAS_TH, BORG_TH, TaskID) %>%
summarise(
completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
.groups = "drop"
) %>%
filter(rotationCondition == "twoHanded")
cor.test(data.rotating.taskCompletion_avg.TH$BORG_TH,
data.rotating.taskCompletion_avg.TH$completion_time, method = "spearman")## Warning in cor.test.default(data.rotating.taskCompletion_avg.TH$BORG_TH, :
## Cannot compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: data.rotating.taskCompletion_avg.TH$BORG_TH and data.rotating.taskCompletion_avg.TH$completion_time
## S = 131579, p-value = 0.2968
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1075767# PAAS vs Accuracy
data.rotating.matched.OH$condition <- "OH"
data.rotating.matched.TH$condition <- "TH"
data.rotating.matched.PAAS_combined <- bind_rows(
data.rotating.matched.OH %>% rename(PAAS = PAAS_OH),
data.rotating.matched.TH %>% rename(PAAS = PAAS_TH)
)
ggplot(data.rotating.matched.PAAS_combined, aes(x = PAAS, y = match_accuracy_result, color = condition)) +
geom_point(alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE) +
labs(
title = "Mental Exertion vs Match Accuracy in Rotating",
x = "PAAS Scale",
y = "Match Accuracy",
color = "Condition"
) +
scale_color_manual(values = c("OH" = "blue", "TH" = "red")) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
# PAAS vs Completion Time
data.rotating.taskCompletion_avg.OH$condition <- "OH"
data.rotating.taskCompletion_avg.TH$condition <- "TH"
data.rotating.taskCompletion_avg.PAAS_combined <- bind_rows(
data.rotating.taskCompletion_avg.OH %>%
rename(PAAS = PAAS_OH),
data.rotating.taskCompletion_avg.TH %>%
rename(PAAS = PAAS_TH)
)
ggplot(data.rotating.taskCompletion_avg.PAAS_combined, aes(x = PAAS, y = completion_time, color = condition)) +
geom_point(alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE) +
labs(
title = "Mental Exertion vs Completion Time in Rotating",
x = "PASS Scale",
y = "Completion Time",
color = "Condition"
) +
scale_color_manual(values = c("RG" = "blue", "NRG" = "red")) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
# BORG vs Accuracy
data.rotating.matched.OH$condition <- "OH"
data.rotating.matched.TH$condition <- "TH"
data.rotating.matched.BORG_combined <- bind_rows(
data.rotating.matched.OH %>% rename(BORG = BORG_OH),
data.rotating.matched.TH %>% rename(BORG = BORG_TH)
)
ggplot(data.rotating.matched.BORG_combined, aes(x = BORG, y = match_accuracy_result, color = condition)) +
geom_point(alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE) +
labs(
title = "Physical Exertion vs Match Accuracy in Rotating",
x = "BORG Scale",
y = "Match Accuracy",
color = "Condition"
) +
scale_color_manual(values = c("OH" = "blue", "TH" = "red")) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
# BORG vs Completion Time
data.rotating.taskCompletion_avg.OH$condition <- "OH"
data.rotating.taskCompletion_avg.TH$condition <- "TH"
data.rotating.taskCompletion_avg.BORG_combined <- bind_rows(
data.rotating.taskCompletion_avg.OH %>%
rename(BORG = BORG_OH),
data.rotating.taskCompletion_avg.TH %>%
rename(BORG = BORG_TH)
)
ggplot(data.rotating.taskCompletion_avg.BORG_combined, aes(x = BORG, y = completion_time, color = condition)) +
geom_point(alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE) +
labs(
title = "Physical Exertion vs Completion Time in Rotating",
x = "BORG Scale",
y = "Completion Time (min)",
color = "Condition"
) +
scale_color_manual(values = c("OH" = "blue", "TH" = "red")) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
#### Preference
data.rotating %>%
select(UserID, behaviour_preference) %>%
distinct() %>%
count(behaviour_preference) %>%
mutate(
percent = n / sum(n),
ncount = paste0(n, "\n", percent_format()(percent))
) %>%
ggplot(aes(x = "", y = n, fill = behaviour_preference)) +
geom_col(width = 1, color = "white") +
coord_polar(theta = "y") +
geom_text(aes(label = ncount), position = position_stack(vjust = 0.5), size = 4) +
labs(
title = "Distribution of Rotation Behaviour Preferences",
fill = "Preference"
) +
theme_void()
#### Comments
data.rotating.preference.summary <- data.rotating %>%
mutate(
behaviour_preference = case_when(
behaviour_preference == "oneHandedPreference" ~ "One Handed Gesture",
behaviour_preference == "twoHandedPreference" ~ "Two Handed Gesture",
behaviour_preference == "noPreference" ~ "No Preference",
TRUE ~ behaviour_preference
)
) %>%
group_by(UserID) %>%
summarise(
behaviour_preference = first(behaviour_preference),
behaviour_feedback = first(behaviour_feedback),
.groups = "drop"
)
kable(data.rotating.preference.summary, caption = "User Feedback Summary - Rotating")| UserID | behaviour_preference | behaviour_feedback |
|---|---|---|
| 1 | One Handed Gesture | I feel more convenient to use one-handed rotation gesture because it is less confusing compared to two-handed rotation gesture, where I had a bit more difficulties in balancing my hands. |
| 2 | Two Handed Gesture | I have more control with the two-handed rotation gesture, it feels more natural. But still feel limited In terms of flexibility upon rotation. I think more gesture such as moving the globe position when both hands are moving simultaneously following the centre of the hands. |
| 3 | One Handed Gesture | More fingers means more calorie burns. But it has limitation with the control, not sure how to solve or give gesture recommendation. |
| 4 | One Handed Gesture | I like one handed better because I have more control to rotate the orientations as I like, as for the gestures I ’ll suggest maybe we can use hands like waving gestures to rotate the globe |
| 5 | No Preference | Both options have their advantages. One hand is simple but little bit harder for complex task like rotation. I think it would be better if I can rotate our pump like rotating door knob |
| 6 | One Handed Gesture | I prefer one-handed gesture one because its easier to imagine the direction. However, the two-handed gesture will be useful in medical field. Especially in surgery. |
| 7 | One Handed Gesture | I prefer one-handed gesture because it is handy and more flexible. |
| 8 | Two Handed Gesture | I prefer two handed because it gives more flexibility. However I feel that two handed takes time to adapt. I think it would be better if we can touch and manipulate directly like aphysical globe. If the globe is far, we can use gaze and pinch to make it nearer, then we can use direct gesture manipulation. |
| 9 | One Handed Gesture | I prefer one handed because it is simpler. |
| 10 | One Handed Gesture | I prefer one handed because it is easier to move the globe from any directions while the two handed it is more difficult because it takes two-hands coordination. Gaze and pinch is convinient enough. |
| 11 | One Handed Gesture | I prefer one handed because thats how I usually operate globe in real life. Unless the two handed uses palms like holding real globes, I’d prefer it. |
| 12 | No Preference | 1. It would be better if we have the option of using two hands, instead of directly using 2 hands. It gives the option of z axis adjustment in the middle of x,y axis rotation. |
- It would be better if we could rotate it with our palm like in iron man 2.
- It would be confusing when you try to combine both rotation and position with the same gestures. |
### SummaryStudy: Scale
Scale Data Preparation
data.scale <- data %>%
inner_join(demographic, by = "UserID") %>%
inner_join(scale_MG, by = "UserID") %>%
rename(
PAAS_MG = Mentally_demanding,
BORG_MG = Physically_demanding
) %>%
mutate(
PAAS_MG = as.numeric(str_extract(PAAS_MG, "\\d+(\\.\\d+)?")),
BORG_MG = as.numeric(str_extract(BORG_MG, "\\d+(\\.\\d+)?"))
) %>%
inner_join(scale_NMG, by = "UserID") %>%
rename(
PAAS_NMG = Mentally_demanding,
BORG_NMG = Physically_demanding
) %>%
mutate(
PAAS_NMG = as.numeric(str_extract(PAAS_NMG, "\\d+(\\.\\d+)?")),
BORG_NMG = as.numeric(str_extract(BORG_NMG, "\\d+(\\.\\d+)?"))
) %>%
inner_join(scale_preference, by = "UserID") %>%
rename(
behaviour_preference = Scale_preference,
behaviour_feedback = Scale_feedback
) %>%
mutate(
behaviour_preference = case_when(
str_detect(behaviour_preference, "Maintain distance") ~ "maintainDistance",
str_detect(behaviour_preference, "Maintain globe") ~ "maintainGlobe",
str_detect(behaviour_preference, "no preference") ~ "noPreference",
TRUE ~ "unknown"
)) %>%
filter(Type == "scaleTask") %>%
select(UserID, TaskID, ActionID, moveGlobeWhileScaling, zoomDirection, Date, ActionStatus, main_scale_x,
main_scale_y, main_scale_z, target_scale_x, target_scale_y, target_scale_z, match_accuracy_result, status,
PAAS_MG, BORG_MG, PAAS_NMG, BORG_NMG, behaviour_preference, behaviour_feedback) %>%
mutate(scaleCondition = if_else(moveGlobeWhileScaling, "movingGlobe", "nonMovingGlobe")) %>%
select(-moveGlobeWhileScaling) %>%
mutate(zoomDirection = as.factor(zoomDirection),
scaleCondition = as.factor(scaleCondition),
status = as.factor(status),
behaviour_preference = as.factor(behaviour_preference))Scale Task Study
### Accuracy
#### Normality
data.scale.matched <- data.scale %>%
filter(status == "Matched")
shapiro.test(data.scale.matched$match_accuracy_result)##
## Shapiro-Wilk normality test
##
## data: data.scale.matched$match_accuracy_result
## W = 0.94732, p-value = 1.64e-06hist(data.scale.matched$match_accuracy_result, breaks = 100,
main = "Histogram (Zoomed)", xlab = "Accuracy",
col = "lightblue", xlim = c(0, 0.5))
plot(density(data.scale.matched$match_accuracy_result),
main = "Density Plot (Zoomed)", xlab = "Accuracy",
col = "blue", lwd = 2, xlim = c(0, 0.5))
# Although the w value is close to 1, the p value is below 0.05 so we reject null hypothesis that the data is normally distributed
# So, we cannot use one way ANOVA, instead, we use Wilcoxon signed-rank test
#### Statistical tests
data.scale.matched.accuracy_avg.long <- data.scale.matched %>%
group_by(UserID, scaleCondition) %>%
summarise(mean_accuracy = mean(match_accuracy_result, na.rm = TRUE), .groups = 'drop')
# %>%
# pivot_wider(names_from = scaleCondition, values_from = mean_accuracy)
data.scale.matched.art <- art(mean_accuracy ~ scaleCondition + (1|UserID), data = data.scale.matched.accuracy_avg.long)
anova(data.scale.matched.art)## Analysis of Variance of Aligned Rank Transformed Data
##
## Table Type: Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## Model: Mixed Effects (lmer)
## Response: art(mean_accuracy)
##
## F Df Df.res Pr(>F)
## 1 scaleCondition 0.43825 1 11 0.5216
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Because p > .05, we fail to reject the null hypothesis.
# There is no evidence that changing the scale condition affects how accurately participants performed.
# An ART ANOVA showed no significant effect of scale condition on mean accuracy, F(1, 11) = 0.44, p = .522, indicating that changing the scale of the globe did not impact participants’ accuracy.
ggplot(data.scale.matched.accuracy_avg.long, aes(x = scaleCondition, y = mean_accuracy, group = UserID)) +
geom_line(aes(color = as.factor(UserID))) +
geom_point(size = 3) +
labs(title = "Paired Accuracy: Moving vs Non-Moving Globe",
x = "Condition",
y = "Match Accuracy") +
theme_minimal()
ggplot(data.scale.matched.accuracy_avg.long, aes(x = scaleCondition, y = mean_accuracy)) +
geom_boxplot(outlier.shape = NA, fill = "lightblue") +
geom_jitter(width = 0.1, size = 2, alpha = 0.7) +
labs(title = "Accuracy by Globe Movement Condition",
x = "Condition",
y = "Match Accuracy") +
theme_minimal()
### Completion Time
data.scale.taskCompletion_avg <- data.scale %>%
group_by(UserID, TaskID) %>%
summarise(
completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
.groups = "drop"
)
shapiro.test(data.scale.taskCompletion_avg$completion_time)##
## Shapiro-Wilk normality test
##
## data: data.scale.taskCompletion_avg$completion_time
## W = 0.69808, p-value < 2.2e-16data.scale.taskCompletion_avg.long <- data.scale %>%
group_by(UserID, scaleCondition, TaskID) %>%
summarise(
completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
.groups = "drop"
) %>%
group_by(UserID, scaleCondition) %>%
summarise(
avg_completion_time = mean(completion_time),
.groups = "drop"
)
# %>%
# pivot_wider(names_from = scaleCondition, values_from = avg_completion_time)
data.scale.taskCompletion.art <- art(avg_completion_time ~ scaleCondition + (1|UserID), data = data.scale.taskCompletion_avg.long)
anova(data.scale.matched.art)## Analysis of Variance of Aligned Rank Transformed Data
##
## Table Type: Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## Model: Mixed Effects (lmer)
## Response: art(mean_accuracy)
##
## F Df Df.res Pr(>F)
## 1 scaleCondition 0.43825 1 11 0.5216
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# An aligned rank transform (ART) ANOVA revealed no significant effect of scale condition on mean accuracy, F(1, 11) = 0.44, p = .522.
# There was no significant effect of scale condition on mean accuracy, F(1, 11) = 0.44, p = .522.
# wilcox.test(
# data.scale.taskCompletion_avg.wide$movingGlobe,
# data.scale.taskCompletion_avg.wide$nonMovingGlobe,
# paired = TRUE,
# alternative = "two.sided"
# )
# data.scale.taskCompletion_avg.long <- data.scale.taskCompletion_avg.wide %>%
# pivot_longer(cols = c(movingGlobe, nonMovingGlobe),
# names_to = "Condition",
# values_to = "completion_time")
ggplot(data.scale.taskCompletion_avg.long, aes(x = scaleCondition, y = avg_completion_time, group = UserID)) +
geom_line(aes(color = as.factor(UserID)), linewidth = 1, alpha = 0.6) +
geom_point(size = 3) +
labs(
title = "Task Completion Time by Condition",
x = "Condition",
y = "Completion Time (minutes)",
color = "UserID"
) +
theme_minimal()
ggplot(data.scale.taskCompletion_avg.long, aes(x = scaleCondition, y = avg_completion_time)) +
geom_boxplot(outlier.shape = NA, fill = "lightblue") +
geom_jitter(width = 0.1, size = 2, alpha = 0.7) +
labs(title = "Accuracy by Globe Movement Condition",
x = "Condition",
y = "Match Accuracy") +
theme_minimal()
### Subjective Measures
#### Physical Exertion
# Spearman’s rank correlation is a non-parametric test.
# It does not assume normal distribution of the variables.
# It works on ranks of the data, not the raw values — so it’s robust against skewed or non-normal distributions.
data.scale.matched.MG <- data.scale.matched %>%
filter(scaleCondition == "movingGlobe")
cor.test(data.scale.matched.MG$BORG_MG,
data.scale.matched.MG$match_accuracy_result, method = "spearman")## Warning in cor.test.default(data.scale.matched.MG$BORG_MG,
## data.scale.matched.MG$match_accuracy_result, : Cannot compute exact p-value
## with ties##
## Spearman's rank correlation rho
##
## data: data.scale.matched.MG$BORG_MG and data.scale.matched.MG$match_accuracy_result
## S = 130858, p-value = 0.2753
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1124638ggplot(data.scale.matched.MG, aes(x = BORG_MG, y = match_accuracy_result)) +
geom_point(color = "steelblue", size = 2, alpha = 0.7) +
geom_smooth(method = "loess", color = "darkred", se = TRUE) +
labs(
title = "Correlation between Physical Exertion and Accuracy (Moving Globe)",
x = "Physical Exertion (BORG_MG)",
y = "Match Accuracy Result"
) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at -0.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 1.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 3.0914e-16## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 0.25## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## -0.015## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1.015## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 3.0914e-16## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 0.25
data.scale.matched.NMG <- data.scale.matched %>%
filter(scaleCondition == "nonMovingGlobe")
cor.test(data.scale.matched.NMG$BORG_NMG,
data.scale.matched.NMG$match_accuracy_result, method = "spearman")## Warning in cor.test.default(data.scale.matched.NMG$BORG_NMG,
## data.scale.matched.NMG$match_accuracy_result, : Cannot compute exact p-value
## with ties##
## Spearman's rank correlation rho
##
## data: data.scale.matched.NMG$BORG_NMG and data.scale.matched.NMG$match_accuracy_result
## S = 176003, p-value = 0.05859
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.1937286ggplot(data.scale.matched.NMG, aes(x = BORG_NMG, y = match_accuracy_result)) +
geom_point(color = "steelblue", size = 2, alpha = 0.7) +
geom_smooth(method = "loess", color = "darkred", se = TRUE) +
labs(
title = "Correlation between Physical Exertion and Accuracy (Non-moving Globe)",
x = "Physical Exertion (BORG_NMG)",
y = "Match Accuracy Result"
) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at -0.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 1.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 1.7288e-16## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 0.25## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## -0.015## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1.015## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 1.7288e-16## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 0.25
data.scale.taskCompletion_avg.MG <- data.scale %>%
group_by(UserID, scaleCondition, PAAS_MG, BORG_MG, TaskID) %>%
summarise(
completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
.groups = "drop"
) %>%
filter(scaleCondition == "movingGlobe")
cor.test(data.scale.taskCompletion_avg.MG$BORG_MG,
data.scale.taskCompletion_avg.MG$completion_time, method = "spearman")## Warning in cor.test.default(data.scale.taskCompletion_avg.MG$BORG_MG,
## data.scale.taskCompletion_avg.MG$completion_time, : Cannot compute exact
## p-value with ties##
## Spearman's rank correlation rho
##
## data: data.scale.taskCompletion_avg.MG$BORG_MG and data.scale.taskCompletion_avg.MG$completion_time
## S = 103893, p-value = 0.003484
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2953543data.scale.taskCompletion_avg.NMG <- data.scale %>%
group_by(UserID, scaleCondition, PAAS_NMG, BORG_NMG, TaskID) %>%
summarise(
completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
.groups = "drop"
) %>%
filter(scaleCondition == "nonMovingGlobe")
cor.test(data.scale.taskCompletion_avg.NMG$BORG_NMG,
data.scale.taskCompletion_avg.NMG$completion_time, method = "spearman")## Warning in cor.test.default(data.scale.taskCompletion_avg.NMG$BORG_NMG, :
## Cannot compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: data.scale.taskCompletion_avg.NMG$BORG_NMG and data.scale.taskCompletion_avg.NMG$completion_time
## S = 93356, p-value = 0.0002369
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.3668193# PAAS vs Accuracy
data.scale.matched.MG$condition <- "MG"
data.scale.matched.NMG$condition <- "NMG"
data.scale.matched.PAAS_combined <- bind_rows(
data.scale.matched.MG %>% rename(PAAS = PAAS_MG),
data.scale.matched.NMG %>% rename(PAAS = PAAS_NMG)
)
ggplot(data.scale.matched.PAAS_combined, aes(x = PAAS, y = match_accuracy_result, color = condition)) +
geom_point(alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE) +
labs(
title = "Mental Exertion vs Match Accuracy in scale",
x = "PAAS Scale",
y = "Match Accuracy",
color = "Condition"
) +
scale_color_manual(values = c("MG" = "blue", "NMG" = "red")) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
# PAAS vs Completion Time
data.scale.taskCompletion_avg.MG$condition <- "MG"
data.scale.taskCompletion_avg.NMG$condition <- "NMG"
data.scale.taskCompletion_avg.PAAS_combined <- bind_rows(
data.scale.taskCompletion_avg.MG %>%
rename(PAAS = PAAS_MG),
data.scale.taskCompletion_avg.NMG %>%
rename(PAAS = PAAS_NMG)
)
ggplot(data.scale.taskCompletion_avg.PAAS_combined, aes(x = PAAS, y = completion_time, color = condition)) +
geom_point(alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE) +
labs(
title = "Mental Exertion vs Completion Time in scale",
x = "PASS Scale",
y = "Completion Time",
color = "Condition"
) +
scale_color_manual(values = c("MG" = "blue", "NMG" = "red")) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
# BORG vs Accuracy
data.scale.matched.MG$condition <- "MG"
data.scale.matched.NMG$condition <- "NMG"
data.scale.matched.BORG_combined <- bind_rows(
data.scale.matched.MG %>% rename(BORG = BORG_MG),
data.scale.matched.NMG %>% rename(BORG = BORG_NMG)
)
ggplot(data.scale.matched.BORG_combined, aes(x = BORG, y = match_accuracy_result, color = condition)) +
geom_point(alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE) +
labs(
title = "Physical Exertion vs Match Accuracy",
x = "BORG Scale",
y = "Match Accuracy",
color = "Condition"
) +
scale_color_manual(values = c("RG" = "blue", "NRG" = "red")) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
# BORG vs Completion Time
data.scale.taskCompletion_avg.MG$condition <- "RG"
data.scale.taskCompletion_avg.NMG$condition <- "NRG"
data.scale.taskCompletion_avg.BORG_combined <- bind_rows(
data.scale.taskCompletion_avg.MG %>%
rename(BORG = BORG_MG),
data.scale.taskCompletion_avg.NMG %>%
rename(BORG = BORG_NMG)
)
ggplot(data.scale.taskCompletion_avg.BORG_combined, aes(x = BORG, y = completion_time, color = condition)) +
geom_point(alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE) +
labs(
title = "Physical Exertion vs Completion Time",
x = "BORG Scale",
y = "Completion Time (min)",
color = "Condition"
) +
scale_color_manual(values = c("MG" = "blue", "NMG" = "red")) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
#### Preference
data.scale %>%
select(UserID, behaviour_preference) %>%
distinct() %>%
count(behaviour_preference) %>%
mutate(
percent = n / sum(n),
ncount = paste0(n, "\n", percent_format()(percent))
) %>%
ggplot(aes(x = "", y = n, fill = behaviour_preference)) +
geom_col(width = 1, color = "white") +
coord_polar(theta = "y") +
geom_text(aes(label = ncount), position = position_stack(vjust = 0.5), size = 4) +
labs(
title = "Distribution of Scale Behaviour Preferences",
fill = "Preference"
) +
theme_void()
#### Comments
data.scale.preference.summary <- data.scale %>%
mutate(
behaviour_preference = case_when(
behaviour_preference == "maintainDistance" ~ "Maintain Globe's Distance",
behaviour_preference == "maintainGlobe" ~ "Maintain Globe's Position",
behaviour_preference == "noPreference" ~ "No Preference",
TRUE ~ behaviour_preference
)
) %>%
group_by(UserID) %>%
summarise(
behaviour_preference = first(behaviour_preference),
behaviour_feedback = first(behaviour_feedback),
.groups = "drop"
)
kable(data.scale.preference.summary, caption = "User Feedback Summary - Scale")| UserID | behaviour_preference | behaviour_feedback |
|---|---|---|
| 1 | Maintain Globe’s Distance | I prefer maintain globe position since it makes me easy to observe the globe closely and clearly, because I think the maintain distance one is not close enough and a bit blurry. |
| 2 | Maintain Globe’s Distance | For me personally I like to use the maintain distance to globe behaviour because its easier to see when observing the surface. But depends on the situation, if we are in a bigger room such as auditorium it will be more managable. But since in this I’m doing it in a small room, its easier to use the maintain distance to globe. |
| 3 | Maintain Globe’s Distance | For the scope of this globe experiment, I prefer “maintain distance…”, because I do not think it is necessary to go inside the globe which is empty. However, the zoom level for “maintain distance…” behaviour needs to be closer or have zoom level control, I.e, “Observing a very small island in the globe, like Bermuda island” |
| 4 | Maintain Globe’s Distance | I like the 2nd options better so we can observe the globe more detail, without being worry about the globe disappear in front of us. |
| 5 | Maintain Globe’s Position | I prefer maintain globe position because the zoom level is larger, so I can easily observe the object |
| 6 | Maintain Globe’s Distance | For specifically observing maps/globes, I prefer the maiaintain distance to globes one because, ithe maximum zoom level is enough for me to observe the surface of the globe. |
| 7 | Maintain Globe’s Distance | I prefer the maintain distance to globe because it is easier to observe t, the zoom level of maintain distance to globe is good. |
| 8 | No Preference | Depends on the situation. I have no preference. Maintain distance is confusing whether the gesture is broken or not at the maximum zoom point. But if the purpose is for observing the globe the maintain distance is better. |
| 9 | Maintain Globe’s Distance | I prefer maintain distance |
| 10 | Maintain Globe’s Distance | I prefer the maintain distance because it is easier to observe the surface in a proper distance |
| 11 | No Preference | It depends, for professionals like maybe government, if they want to observe details, it would better use maintain globe. But for casual users, they would not like the globe zoomed through their heads, they would like maintain distance better. So, I have no preference. |
| 12 | Maintain Globe’s Distance | 1. It would be better if the limit of the zoom is very close (increase the limit) to our face or at least give the option to. |
- The DPI and the speed of scaling in and out need to be adjusted. It is more into the callibration of the hands gesture and the actual object.
- It is difficult to combine the zoom gestures and the rotation with two hands. |
### Summary